The present disclosure is related generally to the field of image formation. More particularly, the present disclosure is related to motion blur modeling for image formation.
When a subject (i.e., an item within the frame of view of the camera) is in motion, motion blur can act on an image captured by the camera like a two dimensional low pass filter, whose spatial frequency characteristic depends both on the trajectory of the relative motion between the scene and the camera and on the velocity vector variation along it. Such issues are particularly relevant in the fields of forensic and security applications where clarity of the image can be highly important.
With the growing availability of supercomputing capabilities on graphic video cards and computing devices, the processing of light need not be all done by mechanical components such as lenses and/or mirrors, but some of this functionality can be performed computationally. However, computational optics can only be as good as the developed mathematical models.
If the models are accurate, then there is no functional difference between the physical and computational optics besides the computation time. However, the mathematical models to capture such motion without blur have not yet been created especially for dynamic, moving image formation.
For example, since current sensors can measure only the energy of light waves, but not their phase, all phenomena having to do with the wave nature of light, like interference and diffraction, cannot be simulated. Mathematical theory can calculate these phenomena, but as yet data has not been able to be provided for its computational applications. Currently, computations are largely limited to the phenomena that can be explained by geometric optics.